A Unified Game-Theoretic Interpretation of Adversarial Robustness
This provides a principled explanation for adversarial robustness, potentially unifying existing methods and revising prior misconceptions, which is significant for researchers in machine learning security.
The paper tackles the problem of understanding adversarial attacks and defenses in deep neural networks by proposing a unified game-theoretic interpretation based on multi-order interactions between input variables, revealing that attacks affect high-order interactions while robustness stems from category-specific low-order interactions.
This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i.e.} the view of multi-order interactions between input variables of DNNs. Based on the multi-order interaction, we discover that adversarial attacks mainly affect high-order interactions to fool the DNN. Furthermore, we find that the robustness of adversarially trained DNNs comes from category-specific low-order interactions. Our findings provide a potential method to unify adversarial perturbations and robustness, which can explain the existing defense methods in a principle way. Besides, our findings also make a revision of previous inaccurate understanding of the shape bias of adversarially learned features.